With projected Long Term Evolution (LTE) traffic to exceed 4 Exabyte per month in 2014, current test and measurements solution vendors are faced with a great challenge to provide real time and scalable solution for LTE service and traffic analysis. Some current solutions involve the installation of very large and complex hardware probes at aggregate network nodes to be able to detect LTE service and traffic degradations and provide root cause analysis and traffic characterization. These hardware probes may fail to meet the increasing traffic volumes at these nodes and are exponentially costing more in terms of processing power needed. Such solutions are projected to cost more than the LTE network deployment cost.
One approach used by test and measurement providers relies on deploying a specialized hardware (Mega) probe at the main aggregate nodes in the LTE network. All the packets from all the different access nodes converge into these aggregate nodes and the Mega probe has to process the packets in real time to identify any degradation or protocol/technology/network errors that impact the LTE service. As the volume of the traffic and the number of access nodes expands, the processing power demand on the Mega probe increases exponentially because packets will typically come from different nodes at different interfaces all mingled together. Sorting and filtering may require processing each packet in real time and keeping it in memory until all packets related to the same session and service are examined and data correlated. Such requirements may strain the processing unit power and memory needs and can lead to an exponential cost increase as the traffic grows. This may result in an increase in the cost of the Mega probe as the network and traffic expand. It is projected that the cost of Mega probes may exceed the cost of the nodes they are monitoring.
Other approaches address the problem by sampling the packets as they arrive. In this case, based on random sampling techniques, only a fraction of the packets is examined. This approach can provide some insight into major chronic problems, but may not allow accurate detection and characterization of the LTE network and service performance and usage.